AAAI Publications, Fourth Annual Symposium on Combinatorial Search

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Improved Prediction of IDA*'s Performance via Epsilon-Truncation
Levi Lelis, Sandra Zilles, Robert Craig Holte

Last modified: 2011-07-05


Korf, Reid, and Edelkamp launched a line of research aimed at predicting how many nodes IDA* will expand with a given cost bound. This paper advances this line of research in three ways. First, we identify a source of prediction error that has hitherto been overlooked. We call it the ``discretization effect''. Second, we disprove the intuitively appealing idea that a ``more informed'' prediction system cannot make worse predictions than a ``less informed'' one. More informed systems are more susceptible to the discretization effect, and in several of our experiments the more informed system makes poorer predictions. Our third contribution is a method, called ``$\epsilon$-truncation'', which makes a prediction system less informed, in a carefully chosen way, so as to improve its predictions by reducing the discretization effect. In our experiments $\epsilon$-truncation rarely degraded predictions; in the vast majority of cases it improved predictions, often substantially.


Heuristic Search; Search Effort Prediction

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